The Impact of COVID-19 on Small Businesses in the US: A Longitudinal Study from a Regional Perspective (2024)

Regional Economic Structure

Researchers have linked regional resilience with socioeconomic and institutional factors (). Some industries are more sensitive than others, and regional sensitivity is the result of a combination of these sectoral sensitivities (Brown and Greenbaum 2017; Martin et al. 2016). For example, based on regional employment data for Canada from 1987 to 2012, research has found that employment loss in each recession was closely associated with industry mix in the preceding growth period. During each recession, manufacturing had much bigger employment losses and a much weaker recovery than the service sector (Ray et al. 2017). Similarly, during COVID-19, some industries, such as accommodation and food services, the arts, entertainment, recreation, and educational services, have been more vulnerable than others (SBA 2020). Wang and Kang (2021) find that metropolitan areas that have higher densities of vulnerable industries are associated with more business losses and more negative impact as perceived by small business owners.

The degree of sectoral specialization also impacts overall regional sensitivity. On the one hand, diversification, the opposite of specialization, reduces the concentration of risk (Groot et al. 2011). On the other, specialization can benefit regional growth by increasing competitiveness and externalities, which consequently increases regional resilience in the long run (Kitsos and Bishop 2018). In particular, regional human capital and innovation efforts can capture internal capabilities simultaneously, reacting ‘innovatively’ to adverse external conditions (Crescenzi et al. 2016). For example, regions endowed with technologically coherent knowledge bases are better prepared to face unforeseen downturns and display adaptive resilience. Local economies tend to be more adaptable if they innovate in those of their industrial and service sectors that have the strongest growth opportunities (Rocchetta and Mina 2019).

Drawing on these studies, we hypothesize that the impact of COVID-19 on small businesses in a region is associated with the region’s economic structure. A region’s economic structure includes several dimensions: its industrial specialization or diversification, its competitiveness (e.g. the proportion of high technology and the extent to which the labor force is highly skilled), and its vulnerability to disastrous events (e.g. the proportion of industries vulnerable to pandemic disruption).

Health Situation and Socioeconomic Vulnerability

Previous research studies have offered ample evidence that resilience in the face of disaster is affected by the degree to which social vulnerability is spread across multiple dimensions: race/ethnicity, gender, income, poverty, education, and social isolation. Social vulnerability exacerbates not only the damage caused by disasters, but also affects people’s access to social and economic resources, and therefore their overall ability to cope with and recover from the impact of disasters (Marshall and Schrank 2014; Peaco*ck et al. 2014; van de Lindt et al. 2018). For example, when natural disasters occur, socioeconomically and physically vulnerable groups experience later evacuation, sustain a greater degree of damage, can access fewer private and public resources for recovery, and demonstrate slower and lower volumes of repair and rebuilding activity (Van Zandt et al. 2012). However, community resilience is highly dependent on the equity of resource distribution, access to financial resources, and the ability of local institutions to adapt to external shocks (Matarrita-Cascante and Trejos 2013).

Like previous disasters, COVID-19 has disproportionately impacted vulnerable communities: those which have a significant number of racial minority members, older adults, homeless, and low-income households (Bambra et al. 2021; Dokhov and Topnikov 2021). Both international and national crises often call attention to the inequalities in the labor market that disproportionately affect individuals from marginalized backgrounds (Cook and Grimshaw 2021; Kantamneni 2020). In the case of COVID-19, preliminary analyses have revealed that its spread and impact on businesses have indeed been unevenly distributed across space (Bartik et al. 2020; Kim 2021; Zhai et al. 2021a). For example, Zhai et al. (2021) show that in the U.S, densely populated areas, such as Los Angeles (CA), Denver (CO), New York City (NY), and Miami (FL), are likely to have more business closures than areas with a lower population density. In addition, permanent closures of entertainment and recreation businesses are significantly fewer than those of retail and trade businesses and food services. Wang and Kang (2021) also find that metropolitan areas with higher densities of vulnerable industries are associated with more business losses and higher negative impacts as perceived by small business owners.

Drawing on these studies, we hypothesize that the impact of COVID-19 on small businesses is highly dependent on the evolving health situation, such as the pandemic’s incidence rate, death rate, and vaccination rate. Furthermore, the proportion of the population that is socioeconomically vulnerable correlates negatively with regional recovery and resilience.

Role of States and Public Policy

Public policies are instrumental in providing precautionary planning against crises, mitigating the worst effects once a crisis has arrived, and transforming and reorientating regions towards recovery. Governments worldwide responded to COVID-19 with a variety of laws, regulations, and assistance programs at different levels (Bourdin et al. 2021). Throughout the U.S, laws that required officials to implement physical distancing resulted in stay-at-home orders, restricted travel, closed schools and nonessential businesses, banned large gatherings, imposed curfews and the use of sanitary cordons, and the mask wearing requirement (Gostin and Wiley 2020). Balla-Elliott et al. (2020) argue that these regulations have been a constraint on firms’ operating decisions, and they suggest that firms would not have remained shut down several months into the pandemic without government-imposed orders.

There are also significant differences in government financial support programs and their implementation. In March of 2020, the U.S. passed the Coronavirus Aid, Relief, and Economic Security (CARES) Act, which provided the largest federal government program for assisting small businesses (e.g. the Payroll Protection Program (PPP)) to blunt the quick and devastating toll caused by the pandemic. Barik et al. (2020) report that 70% of respondents anticipated taking advantage of aid programs like the PPP. However, aid programs are far from perfect, and many respondents anticipated they would have problems accessing the aid. Based on the data of 120,000 firms in 60 countries, Cirera et al. (2021) find that although governments around the world have implemented a wide range of policy support measures, their reach has been limited, especially for more vulnerable firms and countries.

Drawing on these studies, we hypothesize that government policies and programs influence how small businesses cope with the impact of COVID-19. Indeed, Zhai et al. (2021b) find that the temporal and spatial patterns of business closure are closely related to the differing scales of the stay-at-home orders imposed. Likewise, Kim (2021) shows that the arrival of COVID-19 in Korea has been accompanied by a decrease in sales of small businesses there. The emergence of pandemic management strategies, including the repeated lifting and reinforcing of lockdowns, has weakened the effectiveness of policies for controlling the spread of infectious diseases. Therefore, it is critical to detangle different policy factors across time and space. For example, on the one hand, social distancing measures and regulations that restrict economic reopening constrain business operations; on the other hand, financial assistance packages improve business owners’ morale and put them in a position to continue trading.

Data

The main source of data on the pandemic’s impact on small businesses at the state level is the SBPS, which is an experimental data product designed and published by the U.S. Census Bureau. The SBPS is a weekly survey soliciting responses from small business owners (all non-farm, single-location businesses that have between one and 499 employees and receipts of $1000 or more) giving their assessment of the impact of COVID-19, the challenges (e.g. to operations and finances) that businesses are facing, and details of the assistance they have applied for and received. The SBPS is conducted in phases, with each phase comprising 9weeks. Phase 1 was initiated on April 26, 2020 and concluded on June 27, 2020. The most recent data at the time of our data analysis covered the week of June 7, 2021 - June 13, 2021, the fourth week of Phase 5. Our data set thus covered full data from the first 4 phases of the SBPS and partial data from Phase 5 (the first 4weeks), thus giving us figures for 40weeks² Our analyses of the data focused on the 48 contiguous states.

To address our research questions, we examined four categories of variables that we hypothesized were associated with the regional resilience of small businesses in the face of COVID-19: (1) economic structure, (2) socioeconomic vulnerability, (3) health situation, and (4) public policy. We obtained these variables from multiple sources, as indicated in Table 1.

Dependent Variables: Impacts on Small Businesses at the State Level

We investigated both subjective assessments of the impact of the pandemic, as perceived by small business owners, and objective measurements, such as businesses’ financial status and the details given of operational disruptions they had suffered. The Overall Sentiment Index (OSI) and the Expected Recovery Index (ERI) are subjective assessments, each of which is derived from one survey question. The former assesses the overall effect of the pandemic on businesses, while the latter summarizes the length of time recovery is expected to take. The other two indexes, the Operational Challenges Index (OCI) and the Financial Stress Index (FSI), assess the overall effect of the pandemic on business operations and the financial difficulties experienced by businesses, respectively. The OCI incorporates responses from multiple survey questions about changes in revenue; paid employment and hours; times during which businesses were closed; and supply chain disruptions. The FSI presents a picture of cash on hand, loans, regular payments missed, and any applications made for financial assistance from various sources to cope with financial stress³. All four indexes have a range of [-1,1], with negative values indicating a negative effect, zero indicating little or no effect, and positive values indicating a positive effect. Supplementary File 1 (SF1) provides descriptive statistics for the four impact indexes at the state level. The impact of the pandemic on small businesses in the 48 U.S. states was negative throughout the study period. As indicated by the smallest absolute value of the mean for the OCI, small businesses seemed to be the least negatively impacted in terms of operational challenges. We also observed a value very close to 0, -0.02, in South Dakota in the week of June 7, 2021-June 13, 2021, the last week of our study period. Supplementary File 2 (SF2) shows the pairwise Pearson’s correlation coefficients among the four indexes. All of them are statistically significant at the 1% level. The OSI is relatively highly correlated with the other three indexes; and the objective indicators, the OCI and FSI, have a high correlation of 0.7. In contrast, they have lower correlations with the subjective indicator, the ERI, and these are 0.51 and 0.49 respectively. Their relationships are further demonstrated by Figure 1(a) and (b), which visualize the statistical distributions of each index and pairwise relationships among the four indexes in the “initial” and “final” week of each Phase, respectively.

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Figure 1.

(a) Distributions of and pairwise relationships among four impact indexes at the “initial” week of each Phase. (b) Distributions of and pairwise relationships among four impact indexes at the “last” week of each Phase.

Figure 2 displays the trajectories of the four indexes averaged across the states. In producing the figure, we coded the first week of Phase 1 as Week 1, and gaps between consecutive phases were taken into account (Phase 1: Week 1-9, Phase 2: Week 15-23, Phase32: Week 27-35, Phase 4: Week 41-49, Phase 5: Week 54-57). All four indexes show sharp increases over the study period, suggesting the negative impact of the COVID-19 pandemic on small businesses became less severe during that time. However, the trajectories do not show anything like a steady linear progression: all indicate a sudden rebound around Phase 3 (November 9, 2020 - January 10, 2021), although the rebounds are less obvious for the OSI and FSI; and all, except for the ERI, show a sharp increase in Phase 1, with the OSI continuing to increase during Phase 2, although not as sharply as during Phase 1. During Phase 3 the OSI is shown to have stabilized and started to decrease. Then, we see the OSI increasing at a moderate pace during Phase 4 and the first 4weeks of Phase 5.

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Figure 2.

Average Trajectories of Four Indexes across the 48 Contiguous U.S. States (Week 1 started on April 26, 2020, and Week 57 ended on June 13, 2021).

In contrast, the ERI, which measures small business owners’ expectations of business recovery, shows a quite different trajectory. The average expectation does not show a sharp increase in Phase 1 – on the contrary, it even decreases significantly before, like the OSI, increasing during Phase 2. Then, in Phase 3, it recovers and stabilizes to levels similar to those found at the beginning of Phase 2, suggesting that in that period small business owners were not optimistic about recovery despite a short period of regaining confidence from August to October 2020. Finally, we see the ERI climbing quickly and consistently in Phases 4 and 5.

Turning to the two objective measures of the impact of COVID-19, we can see that the average OCI starts at −0.4, increases dramatically during Phase 1, stabilizes during Phase 2, and decreases during Phase 3. This indicates that although the operational challenges faced by small businesses weakened to a significant extent from April to June 2020, they seem to have made a comeback during the period November 2020 - January 2021. In Phase 4, the average OCI increases to a level not seen before and stabilizes at around −0.1 in the first 4weeks of Phase 5. This level represents a 76.8% increase from −0.4 at the beginning of the survey in April 2020 and comes the closest amongst all four indices to the baseline of 0, representing little or no effect on small businesses. The average FSI increases in Phase 1, stabilizes in Phase 2, and decreases slightly in Phase 3. Since the SBPS had, by Phase 4, stopped asking questions necessary for the construction of the FSI, there is no way of knowing how the small businesses fared in facing up to financial stress in the last part of the research period.

The evolution of the four impact indexes was not uniform across the 48 U.S. states. Figure 3 shows significant differences in the spatial distribution of each index over the study period. For instance, while the states on the northeastern coast initially fared the worst in handling business operational challenges, they improved so much that they escaped out of the first quintile. However, small business owners in these states fared the worst according to their assessments of the pandemic’s impact at the beginning and the end of the study period.

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Figure 3.

Spatial distribution (quintile choropleth map) of each index for the 48 contiguous U.S. States. (a) OSI - Week 1: 2020/04/26-2020/05/02, (b) OSI - Week 57: 2021/06/07-2021/06/13, (c) ERI - Week 1: 2020/04/26-2020/05/02, (d) ERI - Week 57: 2021/06/07-2021/06/13, (e) OCI - Week 1: 2020/04/26-2020/05/02, (f) OCI - Week 57: 2021/06/07-2021/06/13, (g) FSI - Week 1: 2020/04/26-2020/05/02, (h) FSI - Week 35: 2021/01/04-2021/01/10.

Independent Variables: Longitudinal Variables in Health Statistics and Public Policies

The ever-changing public health situation for each state is measured by the COVID-19 infection rate (IR), the infection case incidence (ICI), the death case incidence (DCI), and the vaccination completion rate (VCR). The average trends for these measurements across the U.S. States are presented in Figure 4. A rebound can be seen for the ICI and the DCI from Week 30 to Week 41, which corresponds to that observed for the four indexes. The low levels of the ICI and DCI, and the fast-growing VCR, also match the lower levels of the negative impact indexes in the last two phases. As expected, the VCR is negatively correlated with the IR, ICI, and DCI, while the DCI is positively correlated with the ICI, with Pearson’s Correlation Coefficient being 0.56, as shown in Supplementary File 3 (SF3).

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Figure 4.

Average trends of COVID-19 severity, federal assistance, and state policies across 48 contiguous U.S. states.

Public policies and their implementation have varied widely across the States and have been frequently changed to deal with the fast-spreading pandemic, as well as to mitigate the economic downturn associated with it. Many of these policies have directly or indirectly influenced small businesses. For instance, a stay-at-home order, which required residents to stay at home and avoid non-essential trips, was enacted in most of the states before Week 1. This would negatively impact many businesses, especially those requiring face-to-face engagement with customers, such as restaurants and hotels. The Stay-at-Home Requirements Index (SHRI) captures the stringency of this order at the state level. As Figure 4 shows, it was at its maximum at the beginning of the study period, quickly declined during Phase 1 (April 2020-June 2020), stabilized in Phase 2 (August 2020-October 2020), increased slightly in Phase 3 (November 2020 - January 2021), and then decreased dramatically to a very low level in Phases 4 and 5 (February 2021-June 2021). The upward trend observed in Phase 3 coincides with worsening health statistics and the reappearance of severe negative impacts on small businesses.

The Workplace Closing Index (WCI), which measures the extent of requiring or recommending the closing of workplaces or working from home, is also included in our analysis, since it directly impacts business operations. Its average trend reflects that of the SHRI. The School Closing Index (SCI), which measures the extent of requiring or recommending the closing of schools and universities, is considered, since it is likely to indirectly impact small businesses through employees having to take time off or even quit jobs to take care of children at home. We see that this index follows a similar pattern to those of the SHRI and the WCI.

Finally, the Face Covering Index (FCI) records the effect of policies requiring the use of facial coverings outside the home, policies that can impact small businesses if these requirements prevent some customers from enjoying their services. The FCI’s average trend during the early phases is distinctive from those of the other three policy indexes: we see it at its lowest in the beginning, and it climbs quickly in Phase 1; by the second week of Phase 2 (August 16-22, 2020), it has peaked and stabilized; but at the beginning of Phase 3 it climbs again to reach its highest level, and remains there for the rest of Phase 3. Face covering restrictions were loosened in Phases 4 and 5, so here the FCI reflects the other three policy indexes, partially due to increased vaccination rates.

Figure 4 also indicates the degree of federal financial assistance received by small businesses in each state. Two rounds of coronavirus pandemic relief from the federal government were available to small businesses during the study period. The CARES Act (2020) was enacted on March 27, 2020 and provided the first round of financial assistance. The second round was provided by the Consolidated Appropriations Act (2021), which was enacted on December 27, 2020. Phases 1 to 3 of the SBPS included questions about assistance received from the first round, while Phase 4 and beyond replaced these with questions regarding the second round of assistance. Figure 4 shows that the percentage of small businesses receiving federal financial assistance after March 13, 2020, during Phases 1-3, quickly climbed to around 78%, whereas by June 2021 only 55% of small businesses had benefitted from the second round of assistance.

The correlation shown amongst these public policies and public assistance variables is mostly moderate. If we look at SF3, we see that the highest correlation is between the WCI and the SCI, with Pearson’s Correlation Coefficient being 0.48. In addition, none of these variables is highly correlated with the public health statistics, and the majority are not significantly correlated with the public health variables at the 5% level. These variations could help explain inter-state differences in the trajectories of the pandemic’s impact on small businesses.

Time-Invariant Independent Variables

A set of state-level cross-sectional variables measuring economic structure, socioeconomic vulnerability, and other demographic and political features were hypothesized to be associated with COVID-19’s impact on small businesses. Table 2 lists some descriptive statistics for these variables.

We considered three variables concerning the economic structure of a state. The first was the degree of industrial concentration, or the opposite of that, industrial diversification. We adopted the Herfindahl–Hirschman Index (HHI), a commonly used index for regional industrial concentration. The HHIs for U.S. states in 2019 ranged from 693 to 875, with states on the western coast, Texas, and Florida occupying the lower end of the spectrum, indicating relatively low levels of industrial concentration. Supplementary File 4 (SF4) presents the spatial patterns for HHIs across the states. The second variable, the HSI, measures the percentage of industries that employ high-wage and high-skilled workers in each state. As shown in Supplementary File 5 (SF5), states on the western coast and northeastern coast have the highest percentages of high-status industries. The third variable, the Essential Index, measures the percentage of jobs in a state’s industries that are deemed essential during the pandemic (mapped in Supplementary File 6 (SF6)).

Socioeconomic vulnerability was measured by three variables: the percentage of vulnerable industries (VI), the percentage of individuals having 3 or more out of 11 risk factors (CRE3), and the percentage of minority employees in essential industries (EssMinority). Supplementary File 7 (SF7) shows the spatial distributions of these variables across the states. For the VI, the average score for the four impact indexes (the OSI, OCI, FSI, and ERI) for each NAICS sector was used to identify the three sectors that were hit hardest by the pandemic. The weekly averages for accommodation and food services (72), educational services (61), and arts, entertainment, and recreation (71) were the lowest, and these sectors were therefore determined to be the most vulnerable to the pandemic. The percentage of jobs in these sectors for all 48 states was calculated and visualized in the choropleth map.

The CRE3 is a measure of community vulnerability, which is also known as the inverse of community resilience. This variable was drawn from the Community Resilience Estimates (CREs), which had recently been developed by the U.S. Census Bureau. The CREs incorporate 11 risk factors (RFs), ranging from socioeconomic characteristics such as income-to-poverty ratio, absence of household caregivers, communication barriers, unemployment, elderly, and lack of health insurance, to housing issues such as overcrowding. These RFs are known to have exacerbated the pandemic’s toll. We used one of the published variables, CRE3, which represented the percentage of individuals with three or more RFs, as a proxy for community vulnerability.⁴ The map indicates that the sunbelt states generally have higher levels of community vulnerability, meaning that households in these states are more vulnerable to the pandemic.

We further looked at the racial composition of essential workers in each state using the index EssMinority. On average, minority workers accounted for 29.54% of all essential workers in an average state. However, the percentage of minority workers covered a wide range (from 6.31% to 64.23%). Sunbelt states generally had values that were at the high end of the spectrum, and states in the northern plain region had values near the lower end.

Other Control Variables

We used the percentage of urban areas, as defined by the U.S. Census Bureau after the 2010 census, as a proxy for urbanization; and we further incorporated the political party of the state governor. Supplementary File 8 (SF8) shows the spatial distributions of these two variables across states. We then tested the bivariate relationships between independent variables. As shown in Supplementary File 9 (SF9), the CRE3 has a strong positive relationship with the EssMinority variable, indicating that those states, where households are vulnerable to disasters, are also likely to have more minority workers in essential industries, which could further exacerbate their vulnerability to the pandemic.

yti=π0i+πw1iWeekti+πw2iWeekti2+πw3iWeekti3+π1iX1ti+⋅⋅⋅+πKiXKti+εti

(1)

π0i=β00+β01Z1i+⋅⋅⋅+β0MZMi+ζ0iπw1i=βw10πw2i=βw20πw3i=βw30π1i=β10π2i=β20…πKi=βK0εti∼N(0, σε2)andζ0i∼N(0, σ02)

Table 3.

Modeling the Temporal Trajectories of COVID-19's impact on Small businesses across 48 Contiguous U.S. States.

Supplementary material:

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the NSF [2029516].

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